Saturday, November 15, 2008

Jeff Han's TED 2006 Talk Presentation and Transcript

"Jeff Han: Unveiling the genius of multi-touch interface design"

I'm really really excited to be here today, because I'm about to show you some stuff that's just ready to come out of the lab, literally, and I'm really glad that you guys are going to be amongst the first to be able to see it in person because I really really think this is gonna change- really change the way we interact with the machines from this point on.

(standing in front of flat table level screen showing a grid)

Now this is a rear-projected drafting table, it's about 36 inches wide, and it's equipped with a multi-touch sensor. Now, normal touch sensors that you see, like on a kiosk or interactive light ports, can only register a kind of one point of contact at a time.

(traces single wavy line on grid screen with finger)

This thing allows you to have multiple points at the same time.

(traces lines with multiple fingers, then traces lines with all of his fingers at once)

They can use both my hands, I can use chording actions, and I can just go right up and use all 10 fingers if I wanted to. There, like that.

Now, multi-touch sensing isn't anything- isn't completely new, I mean, people like Bill Buxton have been playing around with it in the '80s. However, the approach I developed here is actually high-resolution, low cost, and probably most importantly, very scalable. So, the technology, you know, isn't the most exciting thing here right now, other than probably its new-found accessibility, what's really interesting here is kind of what you can do with it, and the kind of interfaces you can build on top of it. So let's see...

So, for instance we have a LavaLamp application here-

(overhead video of Han pushing and squeezing colored blobs on light table)

Now you can see, I can use both of my hands to kind of squeeze together, and put the blobs together.

(holds hands at bottom of screen, heat of fingers brightens colors, brighter colored blobs remain on screen, then he pushes two small blobs together to make one big one)

I can inject heat into the system here, or I can pull it apart with two of my fingers, it's completely intuitive, there's no instruction manual, the interface just kind of disappears. This started out as kind of a screensaver app that one of the PhD students in our lab, Ilya Rosenberg, made. But I think its true identity comes out here.

(fiddles around with blobs some more)

Now what's great about a multi-touch sensor is that, you know, I could be doing this with as many fingers here, but of course multi-touch also inherently means multi-user. So Chris could be out here interacting with another part of Lava while I kind of play around with it here. You can imagine a new kind of sculpting tool, where I'm kind of warming something up, kind of making it malleable, and then kind of letting it cool down and solidifying in a certain state. (fiddles more) Google should have something like this in their lobby. (laughter)

(tabletop cuts to grid)

I'll show you something a little more of a concrete example here- as this thing loads-

(shows photo images scattered haphazardly around the screen, using both hands, Han pushes photos around on the screen)

Now this is a photographer's light box application. Again, I can use both of my hands to kind of interact and move photos around. But, what's even cooler-

(uses fingers to 'grab' two corners of one of the photos and 'pulls' it to full screen size)

is that, if I have two fingers, I can actually grab a photo and then stretch it out like that really easily. I can pan, zoom, and rotate it effortlessly.

(slides piles of photos around)

I can do that grossly with both of my hands,

(pulls photo out of stack & enlarges it)

or if I can do it just with two fingers on each of my hands together.

(grabs empty space around photos & zooms in and out of canvas)

If I grab the canvas I can kind of do the same thing- stretch it out- I can do it simultaneously, where I'm holding this down-

(holds pile of photos down while pulling out another)

-and gripping on another one, stretching this out like this.

Again, the interface just disappears here. There's no manual. This is exactly what you kind of expect, especially if you haven't interacted with a computer before. Now, when you have initiatives like the hundred dollar laptop, I kind of cringe at the idea that we're gonna introduce a whole new generation of people to computing with kind of this standard mouse-and-windows pointer interface. This is something that I think is really the way we should be interacting with the machines from this point on. (applause)

(taps bottom of screen, graphical keyboard appears on screen, he types caption 'my vacation' onto one photo, which he then pulls off and puts on top of screen. )

Now, of course, I can bring up a keyboard, and I can bring that around, put that up there. Now- obviously this is kind of a standard keyboard, but of course I can re-scale it to make it work well for my hands. And that's really important, because there's no reason in this day and age that we should be conforming to a physical device. I mean, that leads to bad things, like RSI. I mean, we have so much technology nowadays that these interfaces should start conforming to us. I mean, there's so little applied now to actually improving the way we interact with interfaces from this point on.

This keyboard is- I- probably actually the really wrong way to go. You can imagine, in the future, as we develop this kind of technology, a keyboard with a kind of automatically drifts as your hand kind of moves away, and kind of really intelligently anticipates which key you're trying to stroke with your hands. So- again, isn't this great? (moves photos around more, pause)

(audience member: "Where's your lab?")

I'm a Research Scientist at NYU, it's in New York.

(switches off photobox application)

(turns on new graphics app- he makes little balls of light by touching the screen, which are brighter the longer he holds his fingers down, and which move in paths according to his finger movements)

Here's an example of another kind of app- I can kind of make these little fuzz dolls- it'll kind of remember the strokes I'm making. Of course I can do it with all my hands. It's pressure sensitive, you can notice-

(makes larger, brighter ball by holding finger down longer, then he zooms out by stretching screen as he did with photos, making balls on screen smaller. once he zooms out, he can make larger balls, then zoom back in, making the older ones their original size.)

But what's neat about that is again, I showed you that two finger gesture that allows you to zoom in really quickly. Because you don't have to switch to a hand tool, or the magnifying glass tool, you can just kind of continuously make things in real multiple scales, all at the same time. (zooms out) I can create big things out here, but I can go back (zooms in) and really quickly go back to where I started, (zooms in further) and make even smaller things here.

Now this is going to be really important as we start getting to things like data visualization. For instance, I think we all really enjoyed Hans Rosling's talk, and he really emphasized the fact that I've been thinking about for a long time too, we have all this great data, but for some reason, it's just sitting there. We're not really accessing it. And one of the reasons why I think that is, is because of things like graphics- will be helped by things like graphics and visualization and inference tools. But I also think a big part of it is gonna be- starting to be able to have better interfaces, to be able to drill down into this kind of data, while still thinking about the big picture here.

Let me show you another app here-

(screen showing Earth globe)

This is something called World Wind, it's done by NASA, it's a kind of- we've all seen Google Earth, this is kind of an open source version of that.

(using previously demonstrated techniques to zoom in and out on various parts of the map, zooming all the way down to San Francisco peninsula, to a street level satellite view of SF)

There are plug-ins to be able to load in different data sets that NASA's collected over the years. But as you can see, I can use the same two fingered gestures to kind of go down and go in really seamlessly- there's no interface, again, it really allows anybody to kind of go in- and, just does what you'd kind of expect, you know? Again, there's just no interface here. The interface just kind of disappears.

(view switches from satellite photo to street contour map)

I can switch to a different data views, that's what's neat about this app here- there you go.

(switches to another satellite data view, this time color enhanced)

NASA's really cool, they have these hyper-spectral images that are kind of false colored so you can- it's really good for determining vegetative use- well, let's go back to this:

(switches to another map, zooming away from SF)

Now the great thing about mapping applications- it's not really kind of 2D, it's kind of 3D. So, again, with a multi point interface, you can kind of do a gesture like this-

(moves hands, and map flips perspectives to a side on view of a coastal mountain range)

so you can kind of be able to tilt around like that, you know. It's not just simply relegated to a kind of 2D panning and motion. Now this gesture that we've developed, again, is just kind of- putting two fingers down- it's kind of defining an axis of tilt, and I can kind of tilt up and down that way. That's something we just came up with on the spot, you know, it's probably not the right thing to do, but there's such interesting things you can do with this kind of interface. It's just so much fun playing around with too.

And, OK, so the last thing I want to show you is, you know, I'm sure we can all think of a lot of entertainment apps that you can kind of do with this thing. I'm a little more interested in the kind of creative applications we can do with this. Now here's a simple application here-

(draws figure on the desk, once he connects the last lines, it fills in w/ a light blue color)

I can kind of draw out a curve. And when I close it, it becomes a character. But the neat thing about it is, I can add control points-

(touches several points of figure, leaving small dots which he is then able to flex and move separately)

And then what I can do is kind of manipulate them with both of my fingers at the same time. (figure moves as if animated) And you notice what it does. It's kind of a puppeteering thing, where I can use as many fingers as I have to kind of draw-
-and make-

(draws second figure above first, rotates and bends it)

Now, there's a lot of actual math going on under here for this kind of to control this mesh and do a lot- do the right thing. I mean, this is a- this technique of being able to manipulate a mesh here, with multiple control points, is actually something that's state of the art, was just released at Siggraph last year, but it's a great example of the kind of research I really love. Kind of- all this compute power to apply to make things kind of do the right things. The intuitive things. Do kind of exactly what you expect.

(wiggles figures around)

So, multi-touch, kind of interaction research, is a very active field right now in HCI. I'm not the only one doing it, there are a lot of other people getting into it, this kind of technology is going to let even more people get into it, and I'm really looking forward to interacting with all you guys over the next few days and seeing how it can apply to your respective fields. Thank you.

Saturday, November 01, 2008

Halloween in the 1960's

Halloween in the 1960's, originally uploaded by lynnmarentette.

Greg and Lynn in the 60's.

Sunday, October 26, 2008

Celestial Economic Sphere, DataViz for the Finance Biz, Truthiness, Behavioral Finance, Gordon Gekko, Quants..

I'm hoping to revisit this topic to create a dynamic, interactive time-line of the history we are living through right now, whatever the outcome.

FYI: This long post has been sub-divided and incorporated into the Economic Sounds and Sights blog, where resources for the time-line will can be found.

The Celestial Economic Sphere, DataViz for the Finance Biz, Truthiness, Behavioral Finance, and Greed.

I came across this imag
e of the Celestial Economic Sphere on a post by Shae Davidson one of the authors of the Creative Synthesis Blog, and thought it was a good symbol for the topics of this post.

The Celestial Economic Sphere was created by Lise Autogena and Joshua Portway for the Black Shoals Stock Market Planetarium. The sphere relies on financial data from 4,000 public companies to create a real-time visual display of stock trading:

"Within this environment, a complex ecology of glowing amoeba-like “artificial life” creatures emerge. The creatures live in a world composed entirely of money and they feed on trading activity. Whenever a stock is traded its’ equivalent star produces food for the creatures – the bigger the trade, the more food is produced. . . .Because the stock market has the kind of cybernetic properties of biological systems and other complex phenomena (feedback loops etc.), it can be studied in the same was as biological systems. This tends to give rise to a sense that the market is somehow a “natural” expression of some fundamental forces.

According to the Black Shoals creators, the planetarium was a response to the collapse of a capital management firm in 1998:

"Watching the news we would hear that the FTSE 100 had slipped today, and we knew that meant bad things would happen in the future, but the connections were invisible and mysterious, like the forces the ancient Babylonians thought were exerted on our lives by the stars of the zodiac. These were the time of the boom and the height of media interest in all things stock market related -- a time where the market was often equated to a kind of ecosystem with a life of its own, and where the internal dynamics of the markets appeared to be more important than it's ties to the real world....So Black Shoals was designed as a kind of parody of the trading desk of the ubermench-the Mount Olympus from which they would survey their creation."

The data visualization incorporates a complex artificial life algorithm or genetic algorithm, which is described in detail in Black Shoals: Evolving Organisms in a World of Financial Data.

Spore meets Wall Street!

Note: Black Shoals is a play on words. Shoals are a group of fish who swim together, but in this case. Black Shoals also refers to the Black Scholes formula based on the work of Fischer Black and Myron Scholes. Black and Scholes built the formula on the previous work of Louis Bachelier, known for the mathematical model of the stock market that gave birth to the concept of stock options.

Catherine Mulbrandon's Visualizing Economics blog provides a variety of data/information visualizations related to the US and World economy that are grounded in reality. She collects and creates interesting representations that make it easier to understand economic concepts. Topics include Nominal vs Real 3-Month Interest Rate 1934-2008, Percentage of World GDP - Past 500 Years, (take a look at the comments for the post), Two Thousand Years of Growth: World Income & Population, and US Inflation: Annual Percent Change (1774-2007).

Maybe part of the solution is better data-viz for the financial biz... and everyone else!

I've been scratching my head over past few weeks watching our nation's financial market tumble into widespread crisis. This post is just a reflection of my curiosity about the matter, as I am not a financial expert. I am a low-risk investor and I do not play the market.

When I first heard about the Wall Street storm, I wondered how things could have Saul Hansell, the author of "Where Were the Quants? How Wall Street Lied to Its Computers".

Were the quants mesmerized by their faulty numbers, insisting that no hurricane was coming our way?

I know that there are cool tools around to support the quantitative gymnastics required to propel Wall Street businesses. The quants responsible for this work on the "street" have graduate degrees from top-tier universities. They are very smart.

How could so many bright and talented people be asleep at the wheel?

It helps to understand how the actions of a few generated a ripple effect that went unnoticed by general public. I am just beginning to understand...

According to Hansell, part of the problem is that the computer models related to risk analysis in the financials simply did not account for the complex changes in the industry. The information entered into the computers was faulty, and in turn, the data modeling was flawed. Important decisions were made based on what streamed out of the computers. One ofHansell's quotes indicates that this was not a mistake:

“There was a willful designing of the systems to measure the risks in a certain way that would not necessarily pick up all the right risks,” said Gregg Berman, the co-head of the risk-management group at RiskMetrics, a software company spun out of JP Morgan. “They wanted to keep their capital base as stable as possible so that the limits they imposed on their trading desks and portfolio managers would be stable.”

I've heard numerous financial experts say that no one ever saw the current storm coming. This difficult for me to believe.
Some experts say that it boils down to psychology. Really?

Perhaps people were too scared to take a serious look at the numbers. Others might have been deluded by their greed.

Maybe no-one felt comfortable speaking out. And now it is clear. The Wall Street empire (emperor) has no clothes.

Memory Triggers
A quick Internet search triggered my memory of various events that were going on over the past few years that contributed to our present situation. I'd like to put all of this in an interactive timeline at some point:

As I mentioned previously, the Celestial Economic Sphere/Black Shoals Stock Market Planetarium was created in response to a 1998 collapse of a capital management firm that sent the London FTSE 100 spiraling downward. If you visit the Black Shoals website, you'll see that there was plenty of information that was available to the public at the time that outlined the economic problems going on at the time.

Did we forget the lessons from Enron?
There was quite a bit of publicly available information around related to the Enron fiasco in 2001. Available on-line is the Enron Explorer e-mail visualizer, which was created from the database of all of the email messages between Enron's senior management team as things were falling apart between 1999-2002.

The following NY Times article (February 2002) will refresh your memory: Enron's Many Strands: The Company Unravels; Enron Buffed Image to a Shine Even as it Rotted from Within

Punishment was not swift, as the trial ended in 2006:
Two Enron Chiefs are Convicted in Fraud and Conspiracy Trial (NY Times, 2006)

"... the executives had sanctioned or encouraged manipulative accounting practices and then crossed the line from cheerleading into outright misrepresentations of financial performance."

"Enron's fall had a far greater impact than on just the energy industry by heightening nervousness among average investors about the transparency of American companies. "The Enron case and all the other scandals and cases that trailed after it may have finally punctured that romance with Wall Street that has been true of American culture for a while now," said Steve Fraser, a historian and author of "Every Man a Speculator: A History of Wall Street in American Life."

Not long after the Enron scandal, questionable practices involving accounting irregularities were discovered at Freddie Mac in 2003, resulting in firing of David W. Glenn, the president, and forced resignations of Leeland C.Brendsel, the chairman and CEO, and Vaughn A. Clarke, the CFO. Initially, the executives were rewarded for bad decision making.


Leeland C. Brendsel's severance package was $24 million, which was eventually protested:
Freddie Mac Severance Pay is Protested

(Alex Berensen, NY Times, 6/13/2003)

" ...Standard & Poor's cut its stock rating on Freddie Mac to avoid from hold. ''We believe the company has been less than forthright in giving investors adequate information regarding recent investigations,'' Standard &Poor's said. ''We are concerned about the magnitude of the investigation and its potential political fallout.'' "

At the same time, Fannie Mae was in trouble. An article in the NY Times, written by Jennifer Lee in 2004, outlined the findings of Federal regulators regarding significant problems in the accounting practices used by Fannie Mae related to amortization and derivatives:

Overseer Says Fannie Mae Due for a Shake-Up

"The accounting techniques used by Fannie Mae effectively resulted in off-balance sheet reserves that were used to smooth earnings to meet the expectations of financial analysts, according to the report."

KPMG was the firm that certified Fannie Mae's books at the time. Fannie Mae fired KPMG, and later sued the company, according to a 2006 NY Times article.

KPMG also certified the books of New Century Financial, which failed in 2007:
Inquiry Assails Accounting Firm in Lender's Fall

"New Century Financial, whose failure just a year ago came at the start of the credit crisis, engaged in “significant improper and imprudent practices” that were condoned and enabled by auditors at the accounting firm KPMG, according to an independent report commissioned by the Justice Department...E-mail messages uncovered in the investigation showed that some KPMG auditors raised red flags about the accounting practices at New Century, but that the KPMG partners overseeing the audits rejected those concerns because they feared losing a client."


In my quest for answers, I found an article written by Floyd Norris that explained a few things related to our current state of economic affairs.

In his August 2007 NY Times post, "The Quants Explain Disaster", Norris reflects on a hedge fund manager's comments regarding some of the unexpected financial bumps experienced on Wall Street at the time, and shares fund manager's comments about a report from Goldman Sachs, "The Quant Liquidity Crunch".

Here is an excerpt from the Goldman Sachs report:
"The speed in which the market reacted to the dislocation was unprecedented and it is not clear that there were any obvious early warning signs. With the benefit of hindsight, there were a few clues before last week that might have hinted at problems to come, including the dramatic rise in implied volatilities and the disruption in other markets and the related potential for contagion. No one, however, could possibly have forecast the extent of deleveraging or the magnitude of last week’s factor returns. "

And another:
"In the coming weeks, we will continue to analyze this extraordinary period. We will also re-evaluate and re-prioritize our research agenda in light of recent events. Stay tuned. As we continue to study these events, we hope to gain additional insights that will help us avoid similar problems in the future."

And from the hedge fund manager:
"Translation: we don’t know what happened to us or what we’re going to do about it, but we really, really, really don’t want to admit that the fundamental premise of our business is fatally flawed and shut down, so we’ll come up with something.
...In other words, what they do works 99% of the time, but the other 1% of the time they blow up — especially since they insist on using a ton of leverage because their brilliant models tell them that what happened last week was a 28-standard deviation event. Hint: IT WASN’T!"


This is where psychology comes into play.

Remember the "Go Go '80's"?
Reaganomics helped improve the economy during that decade, and as a result, which helped us forget how bad it was in 1981 and 1982.


Some of us forgot that this led us to 1987, the year of Black Monday. The date was October 19, 1987. The stock markets around the world crashed to the ground, but by the end of the year, there was an uptick.
Carlson, Mark (2007) "A Brief History of the 1987 Stock Market Crash with a Discussion of the Federal Reserve Response,"Divisions of Research & Statistics and Monetary Affairs Federal Reserve Board, Washington, D.C.

Black Monday Ten Years After: The Motley Fool's 1987 Timeline

(The above resources were found on Wikipedia.)

1987 is also known for Gordon Gekko, a fictional corporate raider played by Michael Douglas in the movie Wall Street. Gekko firmly believed that greed is good. Ironically, the events in the movie forshadow the scandals that have been played out on Wall Street and corporate America over the past 21 years. Below is a quote from Gekko's speech, and the corresponding video clip I found on YouTube.

"The point is, ladies and gentlemen, that greed, for lack of a better word, is good. Greed is right, greed works. Greed clarifies, cuts through, and captures the essence of the evolutionary spirit. Greed, in all of its forms: greed for life, for money, for love, knowledge, has marked the upward surge of mankind". - Gordon Gekko
(Memorable Quotes from Wall Street)

(I will take down this video if I'm notified that it violates someone's copyright.)

Humans are complex creatures. It would be challenging to create a behavioral finance application that could account for and predict various psychological and sociological scenarios. What would represent a constant? What characteristics, traits, behaviors, and inclinations would play as variables?

Here are a few:
It is appropriate to say that "greed" is an important human variable that should be incorporated into this affective/behavioral/financial applications.

Here are a few more:
"herd mentality", "politics", "power", "control", "need for constant adrenaline rush", "sins of omission", "sins of commission", "consumer confidence", "illusion of stability", "cluelessness", and of course, "

I don't intend this to be a joke. In real life, this would be a serious endeavor. I''m realistic to know that Wall Street quants would not waste their time trying to figure out how to quantify the concept of truthiness.

Maybe they should!

Here is an example of something I think touches upon the usefulness of the "truthiness" concept.

Although the following article was written in 2003, it holds up well in 2008.
The seeds of truthiness were planted well before Stephen Colbert came up with the word:

What the Quants Don't Learn in College
(Emanual Derman, Risk Magazine-Trends July 2003/Volume 16/No7

"The only universally applicable law is that of approximate similarity, which states that the best estimate of the unknown market value of a security is the price of another security that's closely similar to it. You need to find (or invent) a model to establish the similarity between two securities by demonstrating the equivalence of their future payouts under a wide range of circumstances....

Suspend disbelief
Although all you have is the limited power of this simple law, you must take your model of similarity seriously. Temporarily, like a fiction reader, you must suspend disbelief in your model. Then, when it's complete, remind yourself that economics and valuation involve the behaviour of people, and think hard about what could go wrong...

On Wall Street, no-one knows what the correct model is, but they go ahead and price and trade anyhow. It's a bit like the trial in Alice in Wonderland...Academics often overemphasise models, but much of the success of a model depends on software engineering....You need live market data, historical time series, databases, input screens and calibration. As a result, for every financial engineer who works on a model you may need three or four more software engineers to make it usable.."

I wonder if any Wall Street companies hired additional software engineers. From what has transpired over the past months, if they did, they were not the right software engineers!

To take a closer look at how software problems might have played a small part in the current situation, I chose to browse the Calyx Software website. Calyx provides software that is used by Freddie Mac and Fannie Mae. There is a treasure trove of information about the quality of this companies mortgage processing software on its support pages.

From my armchair analysis of the types of errors, it seems that it is not difficult for mortgage brokers to unknowingly make errors when determining a potential borrower's risk. The Freddie Mac Loan Prospector looks like it was rolled out before important errors were discovered. In my opinion, it was designed without the capacity to prevent critical errors.

Because the Catlx support page lists many of the problems as common, it is likely that the Freddie Mac Loan Prospector was designed with a lower level of error prevention than expected for this sort of transaction. To fix this problem after the fact, the support pages offer help solutions, but some of the solutions are quite complex.

Common Problems with the Freddie Mac Loan Prospector
Second home is not included in ratios

Credit Agency Missing from the Freddie Mac Loan Prospector
Credit Agency or Lender is Missing from Point

Little things that waste time

Must-watch video
If you have about an hour, watch this discussion between Charlie Rose, Floyd Norris, Mohamed El-Erian, Gretchen Morgenson, and Nouriel Roubini, which aired at the time of the Fannie Mae and Freddie Mac "bailout" decision.

According to Floyd Norris, "The senior managers of the banks assumed that the wizzes under them had their financial models which proved that they did not have any value at risk, and had nothing to worry about, and they believed all of this. And now what they believed looks like nonsense, and you wonder why they did. And that left those banks very exposed... In a lot of cases, they thought they were making money, but they really weren't."

The one thing that is clear to me is that our current economic models no longer function. We can't put the blame on the quants, or the politicians, or the greedy Wall Street leaders. We can't put the blame on new homeowners with low-incomes, or pressured lenders.

Right now, there is a high level of uncertainty, and we do not have anything tangible that ensures that things will be OK. This problem can not be solved quickly. We need better models that can support effective economic decision-making on Wall Street, Main Street, and everywhere inbetween.

The problem of preventing future economic disasters won't be solved by politicians, government officials, economists, and Wall Street leaders. The general public is strongly against the rescue bailout. It simply is too difficult to trust those we've blamed.

My solution at the moment?

We need to stretch our thinking and cast a wide net. This will require an interdisciplinary approach, and include people from a variety of disciplines, who are untainted by monetary scandals and have innovative minds, who care about future generations, and who believe strongly that in a democratic society, all citizens must have access to accurate, understandable information in order to make effective decisions - in all aspects of their lives.

Who might these people be? University researchers, practitioners in the workplace, graduate students, soccer moms, grandpas...with experience in areas such as finance, psychology, history, geography, urban planning, business, economics, banking, sociology, computer science, human-computer interaction, information visualization, graphic arts, & communications.

It is up to everyone to wake up and take action in some way.


Behavioral Finance: Benefiting from Irrational Investors
(Julia Hanna, Harvard Business School)
"Behavioral finance replaces the traditional and idealized idea of rational decision makers with real and imperfect people who have social, cognitive, and emotional biases. The resulting inefficiencies in the capital markets can create opportunities for investment managers and firms."

Behavioral Finance: A Review and Synthesis pdf
(Avanidhar Subrahmanyam, 2006)

The Behavioral Finance Hoax pdf
(Richard Michaud)

Detailed presentation, includes theories and formulas:

A Survey of Behavioral Finance
(Nicholas Barberis, Richard Thaler, presented by Ryan Samson, CalTech)

Behavioral Finance at JP Morgan

(Malcolm P. Baker, Aldo Sesia Jr. 2007, Harvard Business Publishing)

Ivan Boesky

Black Shoals/Scholes Related
The Midas Formula (BBC, 1999)
Midas Formula Program Transcript
Exibition Puts Stocks in Lights
Cefn Hoile

Jean-Philippe Rennard
Book: Handbook of Research on Nature Inspired Computing for Economics and Management
(Rennard is the editor)
Ants Viewer
Social Insects and Self Organization
Genetic Algorithm Viewer
Artificial Life and Genetic Algorithm Links and Resources

21 Ways to Visualize and Explore Your E-mail Box
(Flowing Data)

Tuesday, September 16, 2008

Sitting at home, eating icecream , infographics, and why people drink..

Via Flowing Data, a video from Current that uses infographics combined with an interesting assortment of associations that tip-toe around the meaning of life...

The longer version, possible remix?

I did a bit of hunting and found some information about these clips:

From the website:

"Le Grand Content examines the omnipresent Powerpoint-culture in search for its philosophical potential. Intersections and diagrams are assembled to form a grand 'association-chain-massacre'. which challenges itself to answer all questions of the universe and some more. Of course, it totally fails this assignment, but in its failure it still manages to produce some magical nuance and shades between the great topics death, cable tv, emotions and hamsters."

"The film is a co-production with Karo Szmit. Narration is by Andre Tschinder. The diagrams are inspired by the site created by Jessica Hagy.
There is also an alternate version with music by Andre Tschinder instead of Aphex Twin."

Narration for the longer clip can be found here.


Royksopp's "Remind Me"

Airport Infographics

Sunday, September 07, 2008

Saturday, August 30, 2008

INTERNET OF SURFACES: Photo Examples of Screens of All Sizes (proof-of-concept)

This batch of pictures isn't yet organized, and there are a few examples that are missing. I will add short annotations and links bit-by-bit.

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